



import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin



@require_jieba
class 							lowerCAmelCase_						(    __A			,       unittest.TestCase				):
       UpperCAmelCase__							:   str  	=    CpmAntTokenizer
       UpperCAmelCase__							:   List[Any]  	=    False
       def    snake_case_			(     self			)	->						Dict:
          super().setUp()

          UpperCamelCase       :    Union[str, Any]		      =      [
              '<d>',
              '</d>',
              '<s>',
              '</s>',
              '</_>',
              '<unk>',
              '<pad>',
              '</n>',
              '我',
              '是',
              'C',
              'P',
              'M',
              'A',
              'n',
              't',
          ]
          UpperCamelCase       :    Dict		      =      os.path.join(self.tmpdirname,     VOCAB_FILES_NAMES['vocab_file']			)
          with open(self.vocab_file,     'w',     encoding='utf-8'			) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]			)			)






       @tooslow
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    Any		      =      CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b'			)
          UpperCamelCase       :    List[Any]		      =      '今天天气真好！'
          UpperCamelCase       :    Dict		      =      ['今天', '天气', '真', '好', '！']
          UpperCamelCase       :    Dict		      =      tokenizer.tokenize(UpperCamelCase__			)
          self.assertListEqual(UpperCamelCase__,     UpperCamelCase__			)
          UpperCamelCase       :    str		      =      '今天天气真好！'
          UpperCamelCase       :    Any		      =      [tokenizer.bos_token] + tokens

          UpperCamelCase       :    List[Any]		      =      [6, 9802, 1_4962, 2082, 831, 244]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__			),     UpperCamelCase__			)

          UpperCamelCase       :    Union[str, Any]		      =      tokenizer.decode(UpperCamelCase__			)
          self.assertEqual(UpperCamelCase__,     UpperCamelCase__			)











'''simple docstring'''







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def       __UpperCamelCase					(      *lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       if not isinstance(lowercase_   ,       lowercase_   ):
              a_						     =			list(lowercase_   )
       for i in range(len(lowercase_   )   ):
              a_						     =			None
       gc.collect()
       if is_xpu_available():
              torch.xpu.empty_cache()
       elif is_npu_available():
              torch.npu.empty_cache()
       else:
              torch.cuda.empty_cache()
       return objects


def       __UpperCamelCase					(      lowercase_       :  Exception   ):
       """simple docstring"""




       a_						     =			[
           'CUDA out of memory.',  # CUDA OOM
           'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.',  # CUDNN SNAFU
           'DefaultCPUAllocator: can\'t allocate memory',  # CPU OOM
       ]
       if isinstance(lowercase_   ,       lowercase_   ) and len(exception.args   ) == 1:
              return any(err in exception.args[0] for err in _statements   )
       return False


def       __UpperCamelCase					(      lowercase_       :  callable = None   ,       lowercase_       :  int = 128   ):
       """simple docstring"""




       if function is None:
              return functools.partial(lowercase_   ,       starting_batch_size=lowercase_   )

       a_						     =			starting_batch_size

       def decorator(*lowercase_       :  str   ,       **lowercase_       :  Dict   ):
              nonlocal batch_size
              gc.collect()
              if is_xpu_available():
                     torch.xpu.empty_cache()
              elif is_npu_available():
                     torch.npu.empty_cache()
              else:
                     torch.cuda.empty_cache()
              a_						     =			list(inspect.signature(lowercase_   ).parameters.keys()   )
              # Guard against user error
              if len(lowercase_   ) < (len(lowercase_   ) + 1):
                     a_						     =			', '.join([F'{arg}={value}' for arg, value in zip(params[1:]   ,       args[1:]   )]   )
                     raise TypeError(
                         F'Batch size was passed into `{function.__name__}` as the first argument when called.'
                         F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'   )
              while True:
                     if batch_size == 0:
                            raise RuntimeError('No executable batch size found, reached zero.'   )
                     try:
                            return function(lowercase_   ,       *lowercase_   ,       **lowercase_   )
                     except Exception as e:
                            if should_reduce_batch_size(lowercase_   ):
                                   gc.collect()
                                   if is_xpu_available():
                                          torch.xpu.empty_cache()
                                   elif is_npu_available():
                                          torch.npu.empty_cache()
                                   else:
                                          torch.cuda.empty_cache()
                                   batch_size //= 2
                            else:
                                   raise

       return decorator




